Short-term landfill methane emissions dependency on wind

Diurnal landfill methane flux patterns across different seasons at a landfill in Southeastern US

Diurnal patterns of methane flux are examined at a landfill in the Southeastern US. Methane fluxes are measured by an eddy covariance (EC) tower during representative one-week periods in three seasons: summer, fall, and winter. Measured methane fluxes are compared with atmospheric pressure, temporal variation of atmospheric pressure, wind shear velocity, and air temperature. Landfill methane flux varies significantly with shear velocity and temporal changes in atmospheric pressure when the atmosphere is neutral. Under unstable atmospheric conditions, air temperature correlates best with methane flux, which is corroborated with an independent dataset of tracer correlation method (TCM) measurements for similar measurement periods. These field data support a mathematical model previously proposed to describe atmospheric effects on methane flux from landfills. The field data also indicate significant diurnal methane flux variations, with daytime fluxes up to 23 times greater than nighttime fluxes. Because the majority of historical TCM measurements of whole landfill methane flux are between 12 pm and 6 pm at this landfill, when daily emissions are highest because of atmospheric effects, average diurnal fluxes might have been overestimated by as much as 73%. Methane emissions are most representative of diurnal average emissions when atmospheric stability is near-neutral, which occurs in the late morning (∼11 am) and in the early evening (∼5 pm) at this site.

Methane emission dynamics from a Danish landfill: The effect of changes in barometric pressure

This study investigates temporal variability on landfill methane (CH₄) emissions from an old abandoned Danish landfill, caused by the rate of changes in barometric pressure. Two different emission quantification techniques, namely the dynamic tracer dispersion method (TDM) and the eddy covariance method (EC), were applied simultaneously and their results compared. The results showed a large spatial and temporal CH₄ emission variation ranging from 0 to 100 kg h^-1 and 0 to 12 μmol m^-2 s^-1, respectively. Landfill CH₄ emissions dynamics were influenced by two environmental factors: the rate of change in barometric pressure (a strong negative correlation) and wind speed (a weak positive correlation). The relationship between CH₄ emissions and the rate of change in barometric pressure was more complicated than a linear one, thereby making it difficult to estimate accurately annual CH₄ emissions from a landfill based on discrete measurements. Furthermore, the results did not show any clear relationship between CH₄ emissions and ambient temperature. Large seasonal variations were identified by the two methods, whereas no diurnal variability was observed throughout the investigated period. CH₄ fluxes measured with the EC method were strongly correlated with emissions from the TDM method, even though no direct relationship could be established, due to the different sampling ranges of the two methods and the spatial heterogeneity of CH₄ emissions.

Diurnal and Seasonal Variation of Area-Fugitive Methane Advective Flux from an Open-Pit Mining Facility in Northern Canada Using WRF

Greenhouse Gas (GHG) emissions pose a global climate challenge and the mining sector is a large contributor. Diurnal and seasonal variations of area-fugitive methane advective flux, released from an open-pit mine and a tailings pond, from a facility in northern Canada, were simulated in spring 2018 and winter 2019, using the Weather Research and Forecasting (WRF) model. The methane mixing ratio boundary conditions for the WRF model were obtained from the in-situ field measurements, using Los Gatos Research Ultra-Portable Greenhouse Gas Analyzers (LGRs), placed in various locations surrounding the mine pit and a tailings pond. The simulated advective flux was influenced by local and synoptic weather conditions in spring and winter, respectively. Overall, the average total advective flux in the spring was greater than that in the winter by 36% and 75%, for the mine and pond, respectively. Diurnal variations of flux were notable in the spring, characterized by low flux during thermally stable (nighttime) and high flux during thermally unstable (daytime) conditions. The model predictions of the methane mixing ratio were in reasonable agreement with limited aircraft observations (R2=0.68). The findings shed new light in understanding the area-fugitive advective flux from complex terrains and call for more rigorous observations in support of the findings.

Siting and ranking municipal landfill sites in regional scale using nighttime satellite imagery

In 2016, about 24.9 million tonnes of solid waste were disposed of in Canadian landfills, where landfill technology is a common choice. This study aims to develop a data-driven GIS-based method that considers spatial, environmental, and economic constraints using study regions derived from night time light data for a 40 km buffer around Regina, Saskatchewan, Canada. Unlike other similar studies, this site suitability study assumes no political or administrative boundaries as inputs. Road network stands as the most decisive factor that accounts for 0.239 of entire weight, followed by protective areas with a total weight of 0.220. The regions that ranked the best for siting new landfills were generally located far from predominant water resources and protected areas, but are in the vicinity of major road networks, but are also far from urbanized regions. The sensitivity analysis showed that, overall, road network and protected areas are the most essential layers in this analysis. For the environmental group, protected areas and water resources are major layers. For the economic group, road network and surface temperature are the most important. The method presented in this study can easily accommodate other data sets based on importance in any given area.

Methodologies for measuring fugitive methane emissions from landfills - A review

Fugitive methane (CH₄) emissions from landfills are significant global sources of greenhouse gases emitted into the atmosphere; thus, reducing them would be a beneficial way of overall greenhouse gas emissions mitigation. In Europe, landfill owners have to report their annual CH₄ emissions, so direct measurements are therefore important for (1) evaluating and improving currently applied CH₄ emission models, (2) reporting annual CH₄ emissions and (3) quantifying CH₄ mitigation initiatives. This paper aims at providing an overview of currently available methodologies used to measure fugitive CH₄ emissions escaping from landfills. The measurement methodologies are described briefly, and the advantages and limitations of the different techniques are discussed with reference to published literature on the subject. Examples are given of individual published studies using different methodologies and studies comparing three or more methodologies. This review suggests that accurate, whole-site CH₄ emission quantifications are best done using methods measuring downwind of the landfill, such as tracer gas dispersion and differential absorption LiDAR (DIAL). Combining aerial CH₄ concentration measurements from aircraft or unmanned aerial vehicles with wind field measurements offers a great future potential for improved and cost-efficient integrated landfill CH₄ emission quantification. However, these methods are difficult to apply for longer time periods, so in order to measure temporal CH₄ emission changes, e.g. due to the effect of changes in atmospheric conditions (pressure, wind and precipitation), a measurement method that is able to measure continuously is required. Such a method could be eddy covariance or static mass balance, although these procedures are challenged by topography and inhomogeneous spatial emission patterns, and as such they can underestimate emissions significantly. Surface flux chambers have been used widely, but they are likely to underestimate emission rates, due to the heterogeneous nature of most landfill covers resulting in sporadic and localised CH₄ emission hotspots being the dominant emission routes. Furthermore, emissions from wells, vents, etc. are not captured by surface flux chambers. The significance of any underestimation depends highly on the configuration of individual landfills, their size and emission patterns.

Development and implementation of a screening method to categorise the greenhouse gas mitigation potential of 91 landfills

A cost-effective screening method for assessing methane emissions was developed and employed to categorise 91 older Danish landfills into three categories defined by the magnitude of their emissions. The overall aim was to assess whether these landfills were relevant or irrelevant with respect to methane emission mitigation through the construction of biocovers. The method was based on downwind methane concentration measurements, using a van-mounted cavity ring-down spectrometer combined with inverse dispersion modelling to estimate whole-site methane emission rates. This method was found to be less accurate than the more labour-intensive tracer gas dispersion method, and therefore cannot be recommended if a high degree of accuracy is required. However, it is useful if a less accurate examination is sufficient. A sensitivity analysis showed the dispersion model used to be highly sensitive to variations in input parameters. Of the 91 landfills in the survey, 25 were found to be relevant for biocover construction when the methane emission threshold was set at 2 kg CH₄ h^-1.

AERMOD as a Gaussian dispersion model for planning tracer gas dispersion tests for landfill methane emission quantification

The measurement of methane emissions from landfills is important to the understanding of landfills' contribution to greenhouse gas emissions. The Tracer Dispersion Method (TDM) is becoming widely accepted as a technique, which allows landfill emissions to be quantified accurately provided that measurements are taken where the plumes of a released tracer-gas and landfill-gas are well-mixed. However, the distance at which full mixing of the gases occurs is generally unknown prior to any experimental campaign. To overcome this problem the present paper demonstrates that, for any specific TDM application, a simple Gaussian dispersion model (AERMOD) can be run beforehand to help determine the distance from the source at which full mixing conditions occur, and the likely associated measurement errors. An AERMOD model was created to simulate a series of TDM trials carried out at a UK landfill, and was benchmarked against the experimental data obtained. The model was used to investigate the impact of different factors (e.g. tracer cylinder placements, wind directions, atmospheric stability parameters) on TDM results to identify appropriate experimental set ups for different conditions. The contribution of incomplete vertical mixing of tracer and landfill gas on TDM measurement error was explored using the model. It was observed that full mixing conditions at ground level do not imply full mixing over the entire plume height. However, when full mixing conditions were satisfied at ground level, then the error introduced by variations in mixing higher up were always less than 10%.

Measuring methane emissions from a UK landfill using the tracer dispersion method and the influence of operational and environmental factors

The methane emissions from a landfill in south-east, UK were successfully quantified during a six-day measurement campaign using the tracer dispersion method. The fair weather conditions made it necessary to perform measurements in the late afternoon and in the evening when the lower solar flux resulted in a more stable troposphere with a lower inversion layer. This caused a slower mixing of the gasses, but allowed plume measurements up to 6700 m downwind from the landfill. The average methane emission varied between 217 ± 14 and 410 ± 18 kg h^-1 within the individual measurement days, but the measured emission rates were higher on the first three days (333 ± 27, 371 ± 42 and 410 ± 18 kg h^-1) compared to the last three days (217 ± 14, 249 ± 20 and 263 ± 22 kg h^-1). It was not possible to completely isolate the extent to which these variations were a consequence of measuring artefacts, such as wind/measurement direction and measurement distance, or from an actual change in the fugitive emission. Such emission change is known to occur with changes in the atmospheric pressure. The higher emissions measured during the first three days of the campaign were measured during a period with an overall decrease in atmospheric pressure (from approximately 1014 mbar on day 1 to 987 mbar on day 6). The lower emissions measured during the last three days of the campaign were carried out during a period with an initial pressure increase followed by a period of slowly reducing pressure. The average daily methane recovery flow varied between 633 and 679 kg h^-1 at STP (1 atm, 0 °C). The methane emitted to the atmosphere accounted for approximately 31% of the total methane generated, assuming that the methane generated is the sum of the methane recovered and the methane emitted to the atmosphere, thus not including a potential methane oxidation in the landfill cover soil.

A Simulation model for estimating methane oxidation and emission from landfill cover soils

Quantification of methane (CH₄) oxidation and emission from landfill cover soils is important for evaluating measures to mitigate anthropogenic greenhouse gas emissions. In this study, a model that combines the multicomponent diffusive equation and Darcy's law, coupled with the dual Monod kinetic equation, was established to simulate CH₄ transport, oxidation and emission in landfill cover soils. Sensitivity analysis was performed to illustrate the influence of model parameters on CH₄ transport, oxidation and emission. The model was then applied to predict CH₄ emissions from several column experiments. The results of the sensitivity analysis showed that a high CH₄ oxidation rate can be obtained with a high V_max of cover soil, even for a low cover soil thickness, and that oxidation efficiency is constant when the thickness of the cover soil becomes greater than a threshold value. The simulated results fitted well with the measured values, confirming that the new model provides a reliable method for estimating CH₄ emissions from landfills.

Atmospheric modeling to assess wind dependence in tracer dilution method measurements of landfill methane emissions

The short-term temporal variability of landfill methane emissions is not well understood due to uncertainty in measurement methods. Significant variability is seen over short-term measurement campaigns with the tracer dilution method (TDM), but this variability may be due in part to measurement error rather than fluctuations in the actual landfill emissions. In this study, landfill methane emissions and TDM-measured emissions are simulated over a real landfill in Delaware, USA using the Weather Research and Forecasting model (WRF) for two emissions scenarios. In the steady emissions scenario, a constant landfill emissions rate is prescribed at each model grid point on the surface of the landfill. In the unsteady emissions scenario, emissions are calculated at each time step as a function of the local surface wind speed, resulting in variable emissions over each 1.5-h measurement period. The simulation output is used to assess the standard deviation and percent error of the TDM-measured emissions. Eight measurement periods are simulated over two different days to look at different conditions. Results show that standard deviation of the TDM- measured emissions does not increase significantly from the steady emissions simulations to the unsteady emissions scenarios, indicating that the TDM may have inherent errors in its prediction of emissions fluctuations. Results also show that TDM error does not increase significantly from the steady to the unsteady emissions simulations. This indicates that introducing variability to the landfill emissions does not increase errors in the TDM at this site. Across all simulations, TDM errors range from -15% to 43%, consistent with the range of errors seen in previous TDM studies. Simulations indicate diurnal variations of methane emissions when wind effects are significant, which may be important when developing daily and annual emissions estimates from limited field data.

Numerical simulations to assess the tracer dilution method for measurement of landfill methane emissions

Landfills are a significant contributor to anthropogenic methane emissions, but measuring these emissions can be challenging. This work uses numerical simulations to assess the accuracy of the tracer dilution method, which is used to estimate landfill emissions. Atmospheric dispersion simulations with the Weather Research and Forecast model (WRF) are run over Sandtown Landfill in Delaware, USA, using observation data to validate the meteorological model output. A steady landfill methane emissions rate is used in the model, and methane and tracer gas concentrations are collected along various transects downwind from the landfill for use in the tracer dilution method. The calculated methane emissions are compared to the methane emissions rate used in the model to find the percent error of the tracer dilution method for each simulation. The roles of different factors are examined: measurement distance from the landfill, transect angle relative to the wind direction, speed of the transect vehicle, tracer placement relative to the hot spot of methane emissions, complexity of topography, and wind direction. Results show that percent error generally decreases with distance from the landfill, where the tracer and methane plumes become well mixed. Tracer placement has the largest effect on percent error, and topography and wind direction both have significant effects, with measurement errors ranging from -12% to 42% over all simulations. Transect angle and transect speed have small to negligible effects on the accuracy of the tracer dilution method. These tracer dilution method simulations provide insight into measurement errors that might occur in the field, enhance understanding of the method's limitations, and aid interpretation of field data.